Autoencoder

An autoencoder is a type of artificial neural network used to learn efficient data encodings in an unsupervised manner. Key characteristics:

• An encoder network compresses the input into a latent code.
• A decoder network reconstructs the input from the code.
• The code is a compact "bottleneck" representation of the input.
• Training minimizes reconstruction loss between input and output.
• Regularization helps avoid just copying inputs.
• Useful for dimensionality reduction and feature learning.

Autoencoders enable:

• Efficient data compression and denoising.
• Discovering latent representations.
• Generating new data samples.

Variations like sparse, convolutional, and variational autoencoders enhance performance. Overall, autoencoders are foundational unsupervised learning tools with broad applications.

See also:

• Neural network
• Unsupervised learning
• Dimensionality reduction
• Generative model